A common way information flows through a network is by the Word-of-Mouth effect which is seen as a powerful tool for spreading influence among customers in marketing [1]}. Customers who succeed in utilizing this effect in order to change others perspectives are considered influencers [2]}. It is possible to model such scenarios as a network, in which its topology plays a crucial role in identifying influencers. This is a large area of study with many standard approaches for encoding the network topology, such as neighborhood and centrality metrics as well as collective inference algorithms [3]}. Due to the rising popularity of deep learning, graph neural networks (GNNs) are extensively used for end-to-end tasks of graph learning [4]}, [5]}. However, the research on influencer detection with GNNs is limited, especially when it comes to networks that evolve in time and when there exist several types of connections in a network. To the best of our knowledge, there exists no research about influencer detection on dynamic attributed edge-colored networks with GNNs.
